The GMM (Gaussian Mixture Model) Scoring operation has many well-known usages. In some implementations of GMM (Gaussian Mixture Model) scoring operation may be applied to automated electronic processing of speech recognition and other acoustic signals. Sometimes a dedicated hardware is provided to accelerate workloads that require high-throughput GMM scoring. Such dedicated hardware is typically specifically designed to handle speech recognition workloads.
In other implementations, GMM (Gaussian Mixture Model) scoring operation may be applied to automated electronic processing of image color density modeling in computer vision. GMM workloads in speech processing are fundamentally different than GMM workloads in image color modeling. Speech recognition involves processing relatively small number of samples (100 samples/sec) with large number of Gaussian clusters (thousands). Image color modeling on the other hand requires the processing of very large number of samples or pixels (in the millions) with small number of Gaussian clusters (less than 64). Accordingly, in cases where speech-specific dedicated hardware is available, such speech-specific dedicated hardware is not suitable to accelerate computer vision tasks.